דרושים » דאטה » data platform tech lead

We are looking for a Backend Engineer with deep data engineering expertise to build and evolve the systems that power product, analytics, experimentation, and AI development.
You will work across production backend services and data infrastructure, taking technical ownership of the full data lifecycle - from event ingestion and real-time processing to orchestration, modeling, quality, and reliable data access. This role combines hands-on backend development with ownership of shared data platform, working closely with backend, frontend, AI, DevOps, analytics, and product teams.
What you'll do:
Build and maintain production-grade Python and Java backend services, APIs, and reactive data-processing pipelines with clear interfaces, resilient failure handling, and comprehensive observability.
Own and evolve batch and streaming data pipelines supporting product analytics, learner insights, experimentation, and AI model training.
Design and maintain trusted data models, shared metrics, and semantic-layer business logic across the analytical data platform.
Define and enforce standards for data contracts, testing, lineage, freshness, and observability.
Develop reliable approaches to schema evolution, backfills, replay, workload distribution, and failure recovery.
Collaborate with DevOps, backend, frontend, AI, analytics, and product teams to deliver reusable data capabilities and maintain clear system boundaries.

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.

Key responsibilities

Lead a team of data scientists, data engineers and product analysts

Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation

Drive excellence, code quality, and best development practices

Provide technical direction and hands-on guidance for complex AI systems

Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences

Ensure AI solutions are safe, scalable, observable, and continuously improving

Lead initiatives around product data analysis and experimentation

Analyze user interactions with product features to improve accuracy, UX, and business impact

Partner with product teams to define metrics, dashboards, and experiments that guide product improvements

Design system architectures for AI-enabled applications at scale

Evaluate and select technologies for AI platforms and data pipelines

Guide the development of prompt engineering frameworks and centralized prompt management

Ensure robust monitoring, evaluation, and feedback loops for AI outputs

Translate product and business goals into technical roadmaps and execution plans

Drive alignment between AI capabilities and measurable product outcomes

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

We are looking for an experienced and hands-on Backend Engineer to be a key player in building high-scale Data Platforms and Products for our business teams.
This role involves working with large datasets and scalable systems, and developing internal tools to enable data-driven decision-making across the company.
The team builds the backend systems and data infrastructure the rest of the company relies on: internal products, APIs, event pipelines, and integrations with external tools. The data comes from the full product, at the volume of over 1.7 million daily active users. The features are multi-system: an API, a pipeline, and often LLM-backed flows that have to stay correct in production.
LLMs and agents are part of the job. We use them to write, review, and debug code, and we ship them inside the product: agents and LLM features that other teams depend on.
Key Responsibilities:
Develop internal tools and products for teams across Connecteam.
Build and maintain services and APIs that support complex, multi-step workflows.
Operate in a real-time, event-driven environment at high volume.
Create and manage data pipelines, including the checks that keep data trustworthy.
Design and ship LLM and agent features: tool use, prompts, evaluation, and the limits around cost, latency, and quality.
Use coding agents as part of normal delivery, and raise the quality of what they produce.
Take ownership of multiple systems and products.
Work in a multi-cloud environment (Azure/GCP/AWS).
Integrate third-party tools with our platform.
Translate ambiguous business requirements into a technical plan and lead it through to production.

realize your potential by joining the leading performance-driven advertising company!
as a data platform engineering team leader on the infrastructure r&d department in our tel aviv office, you'll lead the engineering team behind the data platform powering the company's reporting, billing, experimentation, ml training, and revenue optimization. you'll shape the technical direction of one of our most critical platforms, grow exceptional engineers, and solve complex data infrastructure challenges at massive scale-building systems that are reliable, scalable, and easy to use.
how youll make an impact:
lead, mentor, and grow a team of talented software engineers while fostering technical excellence and ownership.
define the team's technical strategy, roadmap, and architecture for one of the company's core data platforms.
own the reliability, scalability, and performance of the data pipelines powering reporting, billing, experimentation, and ml training.
partner with engineering, product, finance, and ml teams to align priorities and deliver high-impact platform capabilities.
drive the adoption of modern data technologies and ai-powered engineering practices to improve Developer productivity and platform evolution.
stay hands-on by reviewing architecture, code, and technical designs, while helping the team solve complex engineering challenges.
guide the evolution of the spark sql platform and shared data infrastructure used across r&d.
represent the team in architecture reviews, hiring, incident postmortems, and technical forums.
why the company?
if you ask taboolars what they love about working here, they'll tell you that they've been empowered to realize their full potential while growing and learning from and with smart and talented people. they'll also share more about:
adam singolda, the company founder and ceo says: "you can copy anything from another business but you can't copy a company's culture."
well-being: enjoy comprehensive benefits (health, etc.), a fully stocked kitchen, and location-specific perks (gym partnerships, parking)
flexibility: we offer a hybrid work schedule with 3 days in-office with an option to come in more often if desired.
work with some of the biggest names: our advertiser clients include wells fargo, honda, pinterest, and expedia. our publisher partners include yahoo, conde nast, fox sports, nbcu, espn, cbs, and e! online.
ready to realize your potential?
the company is an equal opportunity employer and we value diversity in all forms. we are committed to creating an inclusive environment for all employees and believe such an environment is critical for success. employment is decided on the basis of qualifications, merit, and business need.
learn more about #taboolalife on linkedin, facebook, instagram, x, youtube, & the company life blog.
about the company:the company empowers businesses to grow through performance advertising technology that goes beyond search and social and delivers measurable outcomes at scale.

In your role as a Data Engineer at Tikal, you'll be actively engaged in the most fascinating and demanding projects for our clients. Handling high-scale operations, heavy traffic, stringent SLAs, and massive data volumes is just the starting point. Our leadership in the realms of Big Data, Streaming Data, and Complex Event Processing (CEP) stems from our adept use of cutting-edge, optimal technologies.

Responsibilities:
Apply software-engineering methods and best practices (Medallion Architecture) in Data Lake management, using tools like DBT.
Develop Data pipelines to ingest and transform data.
Manage and enhance orchestrated pipelines across company
Data Modeling: Developing data models to ensure efficient storage, retrieval, and processing of data, often involving schema design for NoSQL databases, data lakes, and data warehouses. This will also include catalogs of data.
Scalability: Ensuring that the big data architecture can scale horizontally to handle large volumes of data and high traffic loads.
Data Security: Implementing security measures to protect sensitive data, including encryption, access controls, and data masking.
Data Governance: Establishing data governance policies to ensure data quality, compliance with regulations, and data lineage.

We're looking for a Senior Team Lead to own two things at the core of our product: the data-pipeline infrastructure that ingests and processes web data at scale, and the agentic AI system that generates Extraction Templates - the mappings that turn any webpage into structured data.

You'll lead a team of 6 engineers spanning Backend, Full-Stack, and AI - coaching, managing, and hiring across both disciplines while staying hands-on, not just directing from a distance. Exceptional internal leadership and people skills are a must: clear communication and real ownership of team health, not just delivery.

What Youll be Doing:

Data Pipelines at Scale
Own end-to-end architecture and delivery of our data-pipeline infrastructure - from system design to large-scale data flow, storage, and operations - already running in production for Fortune 500 customers.
Continuously analyze and improve the efficiency, scalability, cost, and stability of pipeline components as volume grows by an order of magnitude or more.
Design and implement supporting interfaces (Skills, Plugins, MCP, UI, API) and platform engineering where needed to deliver pipeline capabilities end-to-end.
Partner closely with the Product and Delivery team to define and prioritize the right pipeline solutions.

Agentic Generation of Extraction Templates
Bring deep, hands-on expertise in LLMs, agentic architectures, evaluation/benchmarking, and modern AI/LLMOps practices; make build-vs-buy and architecture calls with authority.
Grow the team's AI capability day to day: actively promote and mentor AI-native development practices.
Continuously improve the Extraction Template generation capability's coverage, accuracy, and scale.

We're building a new orchestration platform for AI datacenters-software that turns a complex, multi-vendor stack of compute, networking, and accelerators into something customers can deploy, operate, and optimize with confidence.
You'll join a small, growing engineering team where every engineer ships. In this hands-on development role, you'll build backend services, contribute to system design, and own features from implementation through production, working closely with experienced engineers.
If you enjoy solving challenging engineering problems, building new products, and seeing your work run at scale in real AI datacenters, this role is for you.
What You'll Do
- Build and maintain core services and features for an orchestration platform spanning compute, networking, and accelerators.
- Translate customer requirements into APIs, workflows, and data models in collaboration with the team.
- Deliver features end-to-end, from prototyping and implementation to testing, deployment, and production support.
- Write reliable, maintainable code and contribute to shared libraries, automated tests, and CI/CD pipelines.
- Contribute to design discussions and make practical implementation tradeoffs around performance, reliability, and simplicity.
- Participate in code reviews, share knowledge, and help improve engineering practices.
- Troubleshoot issues and improve service performance, observability, and operational readiness.

Since 2011, Morning has helped freelancers and small businesses manage their finances: invoices, payments, expenses and accounting. Today, 170,000 business owners use our platform. Every interaction generates data. So far, that's added up to more than 20 billion events. Our data platform turns this data into the foundation that our product, Customer Experience, finance, and AI teams rely on. As a data Engineer, you'll build and run the pipelines behind it, while helping shape our next-generation company brain: a unified place where people and AI agents can access Morning's knowledge and get reliable answers. We're a small team, which means your work reaches production early and has an impact across the company, with guidance and code reviews from senior engineers along the way.
What you'll do Build and maintain batch and streaming pipelines on Databricks. Transform and model data from our services into clean, reliable and accessible tables. Build data quality checks and monitoring to ensure the reliability of our data. Deploy and maintain production pipelines through CI/CD. Document data models and tables to make data discoverable and easy to use across the company. Identify opportunities to improve pipeline performance, reliability and cost efficiency. Our stack: Python, SQL, Spark, Databricks, AWS, Terraform, TeamCity. We use Claude in day-to-day development.
Advantages:
Experience with serverless architecture. Databricks experience. AWS experience. Terraform or other infrastructure as code. Proficiency with SQL and NoSQL databases and CDC. TypeScript and Node.js. Experience building and managing AI agents. Experience with Amazon Bedrock AgentCore.